Determinants of Water Price, Contract Choice and Crop Production Inefficiency in Groundwater Irrigation Markets in Bangladesh Md. Saidur RahmanPhD Research Scholar at SSD, AndTeacher, Bangladesh Agricultural University

Supervisory CommitteeSupervisor: Dr. M. A. Sattar MandalProf., Agril. Econ., Former VC of BAU & Former Member, Planning Commission, Bangladesh

Co-supervisors 1. Dr. Humnath Bhandari Scientist, IRRI, Dhaka Office

2. Professor Dr. Kei Kajisa School of International Politics, Economics and Communication, Aoyama Gakuin University, Japan

Area 147 thousand sq kmPopulation 150 millionLand typeCultivable land

Floodplains (80%)8.2 mha

Major crops

Irrigated area

Rice (about 75%), wheat, maize, potato, jute, etc.

6.05 mha (75% of total CL)

Agril. GDP

Govt. priority

18.59% in 2009-10

Self-sufficiency in foodgrain by 2013

Some Features of Bangladesh

Density: 1015/sq km

Staple food: Rice

Irrigation development in BangladeshUp to 1950: Swing baskets, doans, etc. Up to 1959, EPWPDA: Flood control and drainage and supplementary irrigation in the monsoon only.Early 1960s: Modern surface irri. (LLP) introduced.In 1961 EPADC: Groundwater irrigation & DTWTFYP (1980-85): Short-gestation, low capital and quick yielding projects, introduced large scale STW.In 1987 and onward: Massive expansion of STW irrigation through market liberalization.

Motivation:

Foodgrain production: 32.9 million tonsBoro rice: 17.81 million tons

54% of Foodgrain production

57% of total rice production

Surface water irrigation (20%): Dam/barrage, LLP, swing basket, doan, etc. Groundwater irrigation (80%): DTW, STW, TP

DTW and TP cover:

13%

STW covers: 67%

STW alone covers 84%

Motivation contd…

The characteristics of the water market

STW owner/seller: Those who own tubewell and sell water.

Irrigate their own land and partners’ landSell excess water to irrigate plots of their

neighbouring farmersMaximise profit from selling waterBuyer: Those who have no tubewell but use

irrigation water from other tubewells.

Nature of WM: Monopoly >Oligopoly > Competitive

Motivation contd…

Characteristics

Seller Buyer

STW Ownership Owner Non-ownerIncome level Higher LowerNon-farm income

Higher Lower

Tenancy type Landlord/own cultivator

Tenant/ owner cum tenant

Farm size Larger SmallerRisk preference Risk neutral Risk averseCash constrained

Less More

Employment Employer at farm level

Employee at farm level

Credit access More LessCrop diversity More LessFamily labour Less More

Operating system

Mode of payment

Rate of payment

Timing of payment

Price range

Input provider

Popularity

Diesel Crop share

¼ share of harvest

After harvest

18000-25000

Seller Getting less Popular

Diesel Fixed charge

Fixed/ha/ season

Beginning the season

15000-18000

Seller Popular

Diesel Two part tariff

Fixed/ha/ season

Beginning the season

12000-13000

Buyer Emerging

Electric. Fixed charge

Fixed/ha/ season

Beginning the season

14000-16000

Seller Popular

Electric. Crop share

¼ share of harvest

After harvest

18000-25000

Seller Not so popular

Existing contracts in irrigation water marketS

ourc

e:R

ahm

an, 2

008

70%

30%

Crop share payment system

Two part tariff payment method

Buyers pay service charge for using STW and use diesel and other irrigation management of his own.

Service charge

• Irrigation introduced in fifties and ¼ crop share as major payment system for irrigation established from that time and it needs evaluations.

• Due to increase in bargaining power of buyer, the payment system is shifting from crop share to cash payment.

• Due to increase in diesel price and electricity price, and also labour price, the mode of payment of irrigation water is changing (crop share to cash payment).

• After more than 30 years, it is high time to evaluate the payment system of irrigation and examine whether the payment has any negative impact on production efficiency or not. We also need to address reasonable price and contract choice.

Literature reviewedAnswers are already known:Profitability, efficiency in irrigation and command area management, cropping patterns, natures of water market, comparative analysis, existing mode of payments, etc.Research gap: Answer still need to be explored (Research questions):Why are diff. payment system emerging? What would be rational price for irrigation?Which factors determine payment systems?Is there any inefficiency in production due to the variations of payment systems?If commitment fails, what will be inefficiency situation?

Data source: Gisselquist, 1991a and Pitman, 1993 (DTW and STW data from 1973-74 to 1986-87); BADC, 2008 (87-88 to 07-08); DAE, 2011 (08-09 to 10-11).

1980-90: 99% PA

1990-00: 17% PA

2000-10: 12% PA

Hypothesis formulation: Price of irrigation water

1.55 million

Water price mainly depends on:1.Bargaining power of the buyer2.Pumping cost3.Land and soil type4.Relationship with seller5.STW command area6.Parcel size & distance7.Water quality

Hypothesis for research question 1.Hyp. 1. Increasing owner density reduces

the use right price of irrigation water.

Payment choice depends on:

Risk : Crop share Cash payment

Interest rates: Crop share Cash payment

Credit inaccessibility: crop share Cash P.

Commitment level: Crop share Cash P.

Asset position: Cash payment crop share

Hypotheses for research question 2.

Hyp. 2. Higher risk in crop production leads to crop share contract.

Hyp. 2.1. Higher interest rate of credit leads buyer to prefer the payment after harvesting (crop share).

Hyp. 2.2. Inaccessibility to credit or unavailability of microfinance lead buyer to prefer the payment after harvesting (crop share).

Hyp. 2.3. If there is less commitment of seller to buyer, buyer prefers cash payment.

Commitment issue

Groundwater level going down

Drought

MPL(1-α)MPL

QL1 QL*

PLP1

Quantity of lab.

MPL

O

Marshallian Inefficiency analysis (1890)

Hayami and Otsuka, 1993

Cheung, 1969

Johnson and many others studies

Source: Hayami, et al. 1993

Production inefficiency issue

Why are we assuming monitoring and supervision are more difficult in water market?

Land rental mkt.

Water mkt.

LandlordTenant

SellerBuyer

Position as a landlord: StrongPunishment: Land use rightAlternative: Very lessAsymmetric info.: More

Position of water lord: Not so strongPunishment: Water use rightAlternative: MoreMoral strength: WeakAsymmetric info.: Less

ActorsActors

Hypotheses for question 3. Hyp. 3.1. The more difficulty in monitoring and supervision of buyer’s farming irrigation, the more inefficiency in production under crop share payment. Hyp. 3.2. Production inefficiency will be higher if there is less commitment of seller to buyer under crop share payment.

Hypothesis for VDSA data:Hyp. 3.3. Production efficiency of tubewell owners are higher than non-owners over the years in Bangladesh.

Rangpur

Rajshahi

Dhaka

Sylhet

ChittagongKhulna

Barishal

Survey Design

Sample selection procedure and size:Village level data: 96 villages *48 upazilas *31 districts * 5 divisions. Household level data: 10 farmers from each village. Total HHs: 960

Divisions Districts Upazilas Unions Villages Households

Chittagong 2 2 4 4 40

Dhaka 8 14 28 28 280

Khulna 6 10 20 20 200

Rajshahi 7 11 22 22 220

Rangpur 8 11 22 22 220

Total (5) 31 48 96 96 960

96 villages 960 Households

960 Irrigation sources960 Largest Plots

Data collectionSeason: Last Boro rice 2013 (Jan-May)Collecting period: May to September, 2013Tools: Soft questionnaire at CAPI with SurveyBe softwareMethod used: FGD and personal interviews

Analytical methods: Descriptive statistics Econometric analysis, regression (OLS, Cobb-Douglas) Probit, Multinomial-Probit Stochastic Frontier Analysis (SFA), Sensitivity analysis,

Tobit

Pre-testing

Division

name2013 2003

PS HS Mad Mar Mos Tem Hos PS HS Mad Mar Mos Tem Hos

Chittagong 1 0 1 0 4 0 0 1 0 0 0 2 0 0

Dhaka 2 0 1 1 4 0 0 1 0 0 1 3 0 0

Khulna 2 1 1 1 4 1 1 2 0 1 1 3 1 0

Rajshahi 2 0 1 1 4 1 1 1 0 1 0 3 1 0

Rangpur 3 1 1 1 6 1 1 2 0 1 1 4 1 0

Total 2 0 1 1 5 1 0 1 0 1 1 3 1 0

Table 1. Village-wise infrastructures in the study areas

0% 20% 40% 60% 80% 100%

Chittagong

Dhaka

Kulna

Rajshahi

Rangpur

All

Agriculture

Business

Service

Unemployed

0% 20% 40% 60% 80% 100%

Chittagong

Dhaka

Khulna

Rajshahi

Rangpur

All

Agriculture

Business

Service

Unemployed

Fig 2. Occupation in 2013

Fig 3. Occupation in 2003

Division 2013 2003

Wage labour

Own farm activities

House-wife

Outside work

Wage labour

Own farm

activities

Housewife

Outside work

Chittagong 42.50 57.50 96.00 4.00 50.00 50.00 97.50 2.50

Dhaka 19.14 70.68 93.64 6.00 29.46 63.21 97.14 2.857

Khulna 32.00 59.25 94.65 5.00 37.50 58.25 98.35 1.65

Rajshahi 36.36 50.00 90.23 10.00 45.00 46.14 91.54 8.45

Rangpur 32.18 51.14 89.36 11.00 35.45 52.95 96.59 3.41

Total 29.73** 58.53 92.19* 7.81* 36.93 55.36 96.00 4.00

Table 2. Farm and non-farm activities of the villagers in the study areas (in percent)

Division Figures in 2013 Figures in 2003 % change over 10 years

Arable land

Irrigated land

Irrigated rice land

Arable land

Irrigated land

Irrigated rice land

Arable land

Irrigated land

Irrigated rice land

Chittagong 95.00 64.50 62.00 92.00 63.75 62.50 3 1 -1

Dhaka 90.79 78.39 74.36 82.00 65.00 66.54 9 13 8

Khulna 93.95 91.30 65.5 92.00 73.50 63.00 2 18 3

Rajshahi 97.82 90.68 74.91 93.00 80.91 67.73 5 10 7

Rangpur 93.05 87.73 69.32 88.00 73.41 58.95 5 14 10

All 93.75 85.46 70.97 88.00 72.29 64.17 5 13 7

Table 3. Pattern of arable land, irrigated land and irrigated rice land in 2003 and 2013

Division name

Equipment (No.)

2013 2003Growth

Rate (%)Annual Growth

Rate (%)

ChittagongSTW 20 8 150 15DTW 1 0 0 0LLP 0 0 0 0

DhakaSTW 34 17 100 10DTW 1 1 0 0LLP 1 1 0 0

KhulnaSTW 82 36 128 13DTW 1 1 0 0LLP 0 1 -100 -10

RajshahiSTW 49 36 36 4DTW 2 1 100 10LLP 0 1 -100 -10

RangpurSTW 65 28 132 13DTW 1 0 0 0LLP 5 14 -64 -6

AllSTW 54 28 93 9DTW 1 1 0 0LLP 1 4 -75 -8

Table 4. Number of irrigation equipments by type in the selected divisions in 2003 and 2013

0

20

40

60

80

100Chittagong

Dhaka

Khulna

Rajshahi

Rangpur

All

0200400600800

100012001400

Chittagong

Dhaka

Khulna

Rajshahi

Rangpur

All

Figure 5. Number of STW in 2013 and 2003

6. STW command areas in 2013 and 2003

Division name

No. of owner(No.)

No. of buyer (No.)

Buyers per

tubewell

Owner education

(Class level)

Buyer education

(Class level)

No. of AWD user

(%)

Chittagong 21.50 260.75 20.88 6.00 6.00 0.00

Dhaka 36.07 321.71 12.85 5.79 5.18 24.64

Khulna 83.85 258.00 5.35 6.10 5.20 1.20

Rajshahi 66.27 406.32 9.89 5.05 5.23 4.32

Rangpur 67.86 394.41 9.49 5.86 4.41 5.77

All 59.63 341.95 10.17 5.71 5.05 9.75

Table 5. Village-wise number of irrigator farmers in the study areas

AWD

Division name

2013 2003 Nearby*

Fixed charge

Crop share

Two part tariff

Fixed charge

Crop share

Two part tariff

Fixed charge

Crop share

Two part tariff

Chittagong 80.00 0 20.00 75.00 0 25.00 80.00 0 20.00

Dhaka 38.89 25.00 36.11 26.66 36.67 36.67 37.84 24.32 37.84

Khulna 58.33 8.33 33.34 60.00 15.00 25.00 60.00 8.00 32.00

Rajshahi 51.72 41.38 6.90 40.74 48.15 11.11 46.67 40.00 13.33

Rangpur 48.48 0 51.52 35.72 7.14 57.14 48.57 2.86 48.57

All 49.61 18.11 32.28 40.37 26.61 33.02 48.48 18.18 33.32

Table 6. Types of Contracts are using currently for the payment of irrigation water (Percent)

Figure 7. Depth of shallow tubewell in the selected divisions in Bangladesh in 2003 and 2013 ( in feet)

Fig. 8. Paddy price in different time periods (Price in BD Taka)

0

100

200

300

400

500

600

700

800

900

1000

2003 2008 2012 2013 2014

Chittagong

Dhaka

Khulna

Rajshahi

Rangpur

14% per year

Division name

No. of flood

Flood duration (days)

No. of drought

No. of storm

No. of disease attack

No of rainfall

Chittagong 0.75 12.50 0.50 0.25 0.00 4.50

Dhaka 1.57 21.68 2.51 0.75 1.75 3.61

Khulna 0.65 12.60 1.65 0.60 1.40 2.95

Rajshahi 1.23 17.36 3.68 0.18 1.32 2.36

Rangpur 1.32 7.318 1.64 0.82 1.36 2.82

Total 1.21 15.13 2.32 0.58 1.42 3.04

Table 7. Average occurrence of natural calamities in the study area per season

Arsenic problem

Division name In 2013 In 2003

Yes No Unknown Yes No Unknown

Chittagong 25 75 0 25 75 0

Dhaka 0 100 0 0 96 4

Khulna 20 80 0 15 80 5

Rajshahi 0 100 0 0 91 9

Rangpur 0 100 0 0 100 0

All 5 95 0 4 92 4

Table 8. Awareness about the salinity of irrigation water in 2003 and 2013 (In percent)

Iron is almost everywhere but farmers think iron is not a problem

Fig 13. Loan sources of the villagers in 2003

Fig 12. Loan sources of the villagers in 2013

0

20

40

60

80

100

120

140

160

180

Chittagong Dhaka Khulna Rajshahi Rangpur All

Bank

Lender

NGO

0

20

40

60

80

100

120

140

160

180

Chittagong Dhaka Khulna Rajshahi Rangpur All

Bank

Lender

NGO

Figure 14. Loan interest rate in

2013

Figure 15. Loan interest rate in

2003

Division name

Group of village elders/village council

Buyer and seller resolve privately

A single trusted individual

A member of the buyer or seller's family

The court system

Others Total

Chittagong 50.00(2)

0 0 50.00(2)

0 0 100(4)

Dhaka 39.53(17)

48.84(21)

2.33(1)

0 6.98(3)

2.33(1)

100(43)

Khulna 41.67(15)

52.78(19)

2.78(2)

0 2.78(1)

0 100(36)

Rajshahi 26.67(8)

63.33(19)

6.67(2)

3.33(1)

0.00 0 100(30)

Rangpur 52.78(19)

44.44(16)

0 0 2.78(1)

0 100(36)

All 40.94(61)

50.34(75)

2.68(4)

2.01(3)

3.36(5)

0.67(1)

100(149)

Table 9. Dispute resolved by different section of the villagers in the study areas (in percent)

I. Research question-1:

Deter. of irrigation water price:The composite form of the model:Yi = α0 + Xiβi + εi 1,

Where  Yi = Price of irrigation water (Tk./ha) in the village iα0 = Intercept βi = Coefficients of the variables of the village iXi = Characteristics of the village i εi = Error term

The empirical models are as follows: Model I. lnYi = α0 + β1lnX1+ β2lnX2+ εi 2,Yi is Price of irrigation, X1 is crop share dummy and X2 is fixed charge dummy Model II for diesel operated tubewell lnYi = α0 + β1lnX1+ β2lnX2+ β3lnX3+ β4lnX4+ β5lnX5+ β6lnX6+ β7lnX7+ β8lnX8+ β9lnX9+ β10lnX10+ β11lnX11+ β12lnX12+ β13 lnX13+ εi 3,whereYi = Price of irrigation water in the village i and i=1 … … … 96X1 = Tubewell owners’ ratioX2 = Command area of a tubewell (decimals)X3 = Percent of people earning remittance (percent)X 4 = Percent of low land area of a village (percent) X5 = Percent of high land area of a village (percent)X6 = Percent of sandy loam soil of a village (percent)X7 = Percent of loam soil of a village (percent)

X8 = Share payment dummy (1= share crop payment, 0=otherwise)X9 = Fixed charge payment dummy (1=fixed charge payment, 0=otherwise) X10 = Number of buyers in a tubewell (number)X11= Number of potential buyers in a tubewell (number)X12 = Buyers’ area of a tubewell (percent)X13 = Tubewell repairing cost (Tk.)εi = Error term Model III for electricity operated tubewell lnYi = α0 + β1lnX1+ β2lnX2+ β3lnX3+ β4lnX4+ β5lnX5+ β6lnX6+ β7lnX7+ β8lnX8+ β9lnX9+ β10lnX10+ β11lnX11+ β12lnX12+ β13 lnX13+ β14 lnX14 + εi 4, Yi = As beforeX1-X13 = As before in model IIX14 = Electricity cost per unit (Tk./kw)εi = Error term

Model selection test:Davidson-Mackinnon J-test for selecting model between OLS and Cobb-Douglas. We run Cobb-Douglas and we get xb (fitted values) and we include it in the OLS model as an explanatory variable. We find this xb is highly significant and thus we get Cobb-Douglas is the best fit model here in this data set.

Test Hypothesis F/Chi2(ᵪ2) values with probability

Decision

1. Omitted variables by using Ramsey RESET test

H0: model has no omitted variables

F(3, 79) = 1.72 and Prob > F =0.1704

We fail to reject null hypothesis

2. Heteroskedasticity test by using Breusch-Pagan/Cook-Weisberg test

Ho: Constant variance

chi2(1)= 0.03Prob > chi2 = 0.8702

We fail to reject null hypothesis

3. Multicolinearity test Ho: VIF values Values are <10 There is no multicolinearity issue

Estimated coefficients of model I

Number of obs = 96F( 2, 93) = 7.55

Prob > F = 0.0009R-squared = 0.1396

Adj R-squared = 0.1211Variables Coefficien

tStd. Err. t P>t

Share crop payment dummy

0.119 0.110 1.08 0.282

Fixed charge payment dummy

-0.197** 0.095 -2.07 0.041

Constant term 9.893(19788.00

)

0.0835 118.55 0

Table 10a-c. Results of first model

Estimated coefficients of model II with diesel operated tubewell

Number of obs = 96F( 13, 82) = 6.33

Prob > F = 0.0000R-squared = 0.5008

Adj R-squared = 0.4216Variables Coefficient Std. Err. t P>tTubewell owner ratio -0.099*** 0.030 -3.26 0.002Command area of a tubewell (decimals) -0.062* 0.033 -1.87 0.065Percent of people earning remittance of a village

0.094*** 0.026 3.69 0.000

Percent of low land area of a village 0.053* 0.027 1.95 0.055Percent of high land area of a village 0.045* 0.028 1.63 0.104Sandy loam soil of a village (percent) -0.018 0.029 -0.63 0.53Loam soil of a village (percent) 0.131*** 0.045 2.89 0.005Share payment dummy 0.091 0.095 0.96 0.342Fixed charge dummy -0.131 0.087 -1.52 0.133Number of buyers in a tubewell -0.104** 0.049 -2.11 0.038Potential buyers in a tubewell (No.) -0.037 0.046 -0.82 0.417Buyers’ area of a tubewell area (percent) 0.027 0.076 0.36 0.723Tubewell repairing cost (Tk) 0.021 0.029 0.72 0.471Constant term 9.572

(14358)0.391 24.51 0

Estimated coefficients of model III with electricity operated tubewell

F( 13, 82) = 5.6Prob > F = 0.0000

R-squared = 0.5357Adj R-squared = 0.4401

Variables Coefficient Std. Err. t P>tTubewell owner ratio -0.117*** 0.034 -3.43 0.001Command area of a tubewell (decimals)

-0.059* 0.034 -1.71 0.093

Percent of people earning remittance of a village

0.087*** 0.027 3.23 0.002

Percent of low land area of a village

0.059** 0.030 1.98 0.052

Percent of high land area of a village

0.036 0.030 1.23 0.223

Sandy loam soil of a village (percent)

-0.048 0.033 -1.46 0.149

Loam soil of a village (percent) 0.079 0.052 1.54 0.129Share payment dummy 0.065 0.105 0.62 0.537Fixed charge dummy -0.155 0.098 -1.59 0.117Number of buyers in a tubewell -0.086 0.053 -1.62 0.11Potential buyers in a tubewell (No.)

-0.040 0.048 -0.83 0.411

Buyers’ area of a tubewell area (%)

-0.006 0.083 -0.07 0.947

Tubewell repairing cost (Tk) 0.018 0.030 0.6 0.548Price of electricity (Tk./kw) 0.381* 0.229 1.66 0.101Constant term 9.457

(12795.00)0.530 17.85 0

II. Research question-2: Contract choice (Probit)General structure of an econometric model is written as follows:Yi

* = Xiβ + ϵi 5,

where Yi* denotes the dependent variable and X denotes

the independent variable of the model. To explain Y 100 percent ϵ is used as an error term and it is assumed that ϵi ~N(0,1).

Models for payment systems:

Yi = αcs + βXics + ϵi where Yi = Share crop payment (1=share crop, 0=otherwise)6,

Yi = αfc + βXifc + ϵi where Yi = Fixed charge payment (1=Fixed charge, 0=otherwise)7,

Empirical probit model for share crop payment system:

Ysc = α0 + β1X1i + β2X2 + β3X3+ β4X4+ β5X5+ β6X6+ β7X7+ β8X8+ε1

9

Where,Ysc Choice of share crop payment system

(1=share crop, 0=otherwise);X1 Flood dummy (1=flooding, 0=otherwise);X2 drought dummy (1=drought, 0=otherwise); X3 arsenic dummy (1=arsenic, 0=otherwise);X4 low land dummy (1=low land, 0=otherwise)X5 interest rate of money lender, and X6 interest rate of NGOα0 intercept;β1-6 coefficients of the independent variables

Empirical probit model for fixed charge payment:

Ycp = α0 + β1X1i + β2X2 + β3X3+ β4X4+ β5X5+ β6X6+ β7X7+ β8X8+ε1 10

Where,

Ycp Choice of share crop payment system (1=cash payment, 0=otherwise);

X1 Likelihood percent of refusing irrigation; X2 diesel price (Tk./lit), X3 electricity price (Tk./unit);X4 drought dummy (1=drought, 0=otherwise)α0 intercept;β1-4 coefficients of the independent variables

Log likelihood = -44.396424 Number of obs = 96LR chi2(6) = 16.92

Prob > chi2 = 0.0096Variables Coef. Std. Err. z P>zFlood dummy 0.740** 0.319 2.32 0.02Drought dummy 0.753* 0.402 1.87 0.061Arsenic dummy -0.661* 0.396 -1.67 0.095Low land dummy -0.703** 0.331 -2.13 0.034Interest rate of money lender

-0.002 0.003 -0.85 0.396

Interest rate of NGO

0.031 0.024 1.29 0.198

Constant term -1.941 0.840 -2.31 0.021

Table 11a_d. Results of probit model of preferring share crop payment system

Hyp. 2.1 Higher risk in crop production leads to crop share contract.

Hyp. 2.2. Higher interest rate of credit leads buyer to prefer the payment after harvesting (crop share).

y = Pr(Share crop payment dummy) (predict)= .19829472

Variables dy/dxStd. Err. z P>z X

Flood dummy 0.207*** 0.088 2.36 0.018 0.48Drought dummy 0.179** 0.079 2.26 0.024 0.73Arsenic dummy -0.160** 0.081 -1.97 0.049 0.27Low land dummy -0.185** 0.081 -2.3 0.022 0.42Interest rate of money lender -0.001 0.001 -0.85 0.397 111.85Interest rate of NGO 0.009 0.007 1.28 0.199 29.77

Marginal effects after probit of preferring share crop payment system

Log likelihood = -58.450951 Number of obs = 96LR chi2(6) = 13.50

Prob > chi2 = 0.0091Variables Coef. Std. Err. z P>z

Likelihood percent of refusing irrigation (%)

0.062* 0.034 1.86 0.063

Diesel price (Tk./lit) -0.057 0.119 -0.48 0.633

Electricity price (Tk./unit)

0.179** 0.094 1.9 0.058

Drought dummy -0.859*** 0.328 -2.62 0.009

Constant term 3.890 8.331 0.47 0.641

Results of probit model of preferring fixed charge payment system

Hyp. 2.3. If there is less commitment of seller to buyer, buyer prefers cash payment.

Commitment

y = Pr(Fixed charge payment dummy) (predict)= .59597877

variable dy/dx Std. Err.

z P>z X

Likelihood percent of refusing irrigation (%)

0.024* 0.013 1.870 0.061 5.62

Diesel price (Tk./lit)

-0.022 0.046 -0.480

0.633 70.18

Electricity price (Tk./unit)

0.069** 0.037 1.890 0.058 3.51

Drought dummy -0.304***

0.101 -3.010

0.003 0.73

Marginal effects after probit of preferring fixed charge payment system

Fig 16. Choice availability

Determinants of contract choice (Village data

It is assumed here on the multinomial model as a series of binary models. That is, evaluate the probability of the alternative j against alternative i for every i≠j. The model started considering the binary model

We get:Pj=F(Xβj)(Pi+Pj)

Application of multinomial probit

Using the expression for

As the response probabilities must sum to 1, we must set the probability of the reference response (j=0) to:

The MNP is obtained through maximum-likelihood estimation:

Mc Fadden (1974) has shown that the log-likelihood function is globally concave, what makes the maximization problem straightforward. The partial effects for this model are complicated. For continuous Xk, we can express like the following equation:

Application of multinomial probit (Village data)Model for choosing any technology:Multinomial Probit Model

Yi,0* = Xi,0β0 + εi,0

Yi,1* = Xi,1β1 + εi,1

Yi,2* = Xi,2β2 + εi,2

Yi =

0 if Crop share; Yi,0* > Yi,1

* & Yi,0* > Yi,2

*

1 if fixed charge; Yi,1* > Yi,0

* & Yi,1* > Yi,2

*

2 if two part tariff; Yi,2* > Yi,0

* & Yi,2* >

Yi,1*

εi ~ type-1 extreme value distribution

Dependent variable(Yi)

Independent variables(Xi)

Contract choices:0 = share crop 1 = fixed charge3 = two part tariff

X1 Yield (kg/ha)X2 Frequent visit (no.)X3 Good relation (no.) X4 Chance of denied irrigation (%)X5 Irrigation cost (Tk./ha)X6 Years of using irrigation (no.)X7 Interest rate of NGOs (Tk.)X8 Flood duration (day)X9 Electricity dummy(1=electricity operated, 0=otherwise)X10 Drought dummy (1=drought, 0=otherwise)X11 Low land dummy(1=low land, 0=otherwise)X12 Distance from upazila dummy (1=10 km, 0=otherwise)

Number of observations = 96

Table 12a_d. Results of mprobit Wald chi2 (24) =24

Probability> chi2 = 0.0725

Contract choice: 0 Base outcome = Share cropPayment system Coefficients Std. Err. z P>z

1 Fixed chargeYield (kg/ha) 0.00108*** 0.00044 2.46000 0.01400Frequent visit (no.) -0.01912 0.01998 -0.96000 0.33900Good relation 0.00011 0.02269 0.00000 0.99600Chance of denied irrigation (%) 0.11020 0.07384 1.49000 0.13600Irrigation cost (Tk./ha) -0.00010** 0.00005 -2.24000 0.02500Years of using irrigation (year) -0.13750*** 0.04389 -3.13000 0.00200Interest rate of NGOs (Tk.) -0.07803* 0.04276 -1.82000 0.06800Flood duration (day) -0.02760** 0.01236 -2.23000 0.02600Electricity dummy 1.91391** 0.94990 2.01000 0.04400Drought dummy -2.07133*** 0.72280 -2.87000 0.00400Low land dummy 1.57627*** 0.62687 2.51000 0.01200Distance from upazila dummy 0.45375 0.54716 0.83000 0.40700Constant term 2.51065 3.01869 0.83000 0.40600

2 Two part tariff

Yield (kg/ha) 0.00070 0.00046 1.53000 0.12600

Frequent visit (no.) -0.04767** 0.02295 -2.08000 0.03800

Good relation 0.05144** 0.02616 1.97000 0.04900

Chance of denied irrigation (%) -0.03440 0.08295 -0.41000 0.67800

Irrigation cost (Tk./ha) -0.00007 0.00005 -1.38000 0.16900

Years of using irrigation (year) -0.02453 0.04666 -0.53000 0.59900

Interest rate of NGOs (Tk.) -0.02196 0.04773 -0.46000 0.64600

Flood duration (day) -0.00132 0.01327 -0.10000 0.92100

Electricity dummy -0.19003 0.77525 -0.25000 0.80600

Drought dummy -0.17991 0.76776 -0.23000 0.81500

Low land dummy -0.21757 0.77941 -0.28000 0.78000

Distance from upazila dummy 0.37720 0.62261 0.61000 0.54500

Constant term -0.80239 3.53152 -0.23000 0.82000

Marginal effects

Case: Crop share payment

Variables

y = Pr(Payment method=0) (predicted outcome of crop share) = .18634181

dy/dx Std. Err. z P>z X

Yield (kg/ha) -0.00020*** 0.00008 -2.48 0.01 6286.90Frequent visit (no.) 0.00515 0.00374 1.38 0.17 45.66Good relation (no.) -0.00232 0.00423 -0.55 0.58 28.62Chance of denied irrigation (%) -0.01586 0.01345 -1.18 0.24 5.62Irrigation cost (Tk./ha) 0.00002** 0.00001 2.39 0.02 19348.70Years of using irrigation (year) 0.02280*** 0.00841 2.71 0.01 31.96Interest rate of NGOs (Tk.) 0.01330 0.00831 1.60 0.11 29.77Flood duration (day) 0.00442* 0.00234 1.88 0.06 15.13Electricity dummy -0.27838 0.20389 -1.37 0.17 0.86Drought dummy 0.25437*** 0.08115 3.13 0.00 0.73Low land dummy -0.21723** 0.09457 -2.30 0.02 0.42Distance from upazila dummy -0.09292 0.10988 -0.85 0.40 0.68

Marginal effects

Case: Fixed charge

Variables

y = Pr(Payment method=1) (predicted, outcome of fixed charge) = .68922906

dy/dx Std. Err. z P>z X

Yield (kg/ha) 0.00021** 0.00010 2.1 0.035 6286.90Frequent visit (no.) 0.00020 0.00468 0.04 0.967 45.66Good relation 0-.00576 0.00553 -1.04 0.297 28.62Chance of denied irrigation (%) 0.03367* 0.01760 1.91 0.056 5.62Irrigation cost (Tk./ha) -0.00002* 0.00001 -1.9 0.057 19348.70Years of using irrigation (year) -0.03442*** 0.01075 -3.2 0.001 31.96Interest rate of NGOs (Tk.) -0.01863* 0.01075 -1.73 0.083 29.77Flood duration (day) -0.00732** 0.00317 -2.31 0.021 15.13Electricity dummy 0.55805*** 0.19105 2.92 0.003 0.86Drought dummy -0.41381*** 0.09850 -4.2 0.000 0.73Low land dummy 0.40726*** 0.11397 3.57 0.000 0.42Distance from upazila dummy 0.08361 0.13574 0.62 0.538 0.68

Marginal effects

Case: Two part tariff

Variables

y = Pr(Pay_method1=2) (predicted outcome of two part tariff) = .12442913

dy/dx Std. Err. z P>z X

Yield (kg/ha) -0.00001 0.00006 -0.19 0.847 6286.90Frequent visit (no.) -0.00535* 0.00323 -1.66 0.098 45.66Good relation 0.00808** 0.00394 2.05 0.04 28.62Chance of denied irrigation (%) -0.01781 0.01206 -1.48 0.14 5.62Irrigation cost (Tk./ha) 0.00000 0.00001 0.05 0.96 19348.70Years of using irrigation (year) 0.01161* 0.00716 1.62 0.104 31.96Interest rate of NGOs (Tk.) 0.00533 0.00717 0.74 0.457 29.77Flood duration (day) 0.00290 0.00208 1.4 0.163 15.13Electricity dummy -0.27967 0.18668 -1.5 0.134 0.86Drought dummy 0.15944** 0.07003 2.28 0.023 0.73Low land dummy -0.19002** 0.08602 -2.21 0.027 0.42Distance from upazila dummy 0.00931 0.08740 0.11 0.915 0.68

Payment methods Observation Mean Std. Dev. Min Max

Crop share, p0 96 0.246 0.243 1.39E-05 0.958907

Fixed charge, p1 96 0.578 0.342 0.001017 0.999854

Two part tariff, p2 96 0.176 0.202 3.65E-09 0.817899

Table 13. Probability of choosing payment methods : Model estimated

Payment systems Frequency Percent Cumulative percent

Crop share ( 0) 23 23.96 23.96

Fixed charge (1) 56 58.33 82.29

Two part tariff (2) 17 17.71 100

Total 96 100 -

Table 14. Frequency distribution of existing payment methods

Research question-3: HH level data

Production inefficiency issue

Production inefficiency estimation:

Qi = F(Li, Ii) (1)

Where Qi is output per hectare i = inputs provided by the farmer i under different payment systems.Li = Labour (man-day) per hectareIi = Irrigation (hour) per hectare, and

F exhibits production function with positive first and second derivatives (F1,F2>0; F11, F22<0). Farmers maximise productivity by using labour and irrigation along with others factors of production but assumed to be constant here in this model.

The specific model is as follows:

Qi =α0+βiXi+ϵ i (2)

Where Qi is output per hectare of the farmer i in a season

X1 is labour (man-day/ha)X2 is irrigation (hour/ha)X3 is seed (kg/ha)X4 is tillage (hour/ha)X5 is chemical fertilizer (kg/ha)X6 is other fertilizer (kg/ha)X7 is insecticide and herbicides (kg or lit/ha)X8 is crop share dummy (1=crop share, 0=otherwise)X9 is fixed charge dummy (1= fixed charge, 0=otherwise)X10 is two part tariff dummy (1= two part tariff, 0=otherwise)

Socioeconomic and socio-demographic factors

X11 is main soil type dummy (sandy loam) (1= sandy loam, 0=otherwise)X12 is main soil type dummy (clay loam) (1= clay loam, 0=otherwise)X13 is main soil type dummy (clay) (1=clay, 0=otherwise)X14 is main land type dummy (medium high land) (1= clay, 0=otherwise)X15 is main land type dummy (high land) (1= high land, 0=otherwise)X16 is farm size (hectare)X17 is family kinship (1=yes, 0=otherwise)X18 is household head education (years of schooling)X19 is irrigation source distance (meter)β1-19 are the unknown parameters to be estimatedAnd, ϵi is error term

Inclusion of supervision from seller:

Qi =α+βXi+γZ1i+ϵ i (3)

Where,

Xi= As beforeZ11 = Share payment*No. of supervision by the sellerZ12 = Fixed charge payment*No. of supervision by the sellerϵi is the sum of two error terms

Inclusion of commitment from seller: Qi =α+βXi+ γZ1i + θZ2i+ϵ i (4)Where,Xi = As beforeZ1i = As beforeZ21 = Crop share payment*Likelihood percent of refusing irrigation water by the sellerZ22 = Fixed charge payment*Likelihood percent of refusing irrigation water by the sellerϵi is error term

Inclusion of transaction cost in irrigation water markets:

Qil= α+βXi+ γZ1i + θZ2i+γW1i+ϵi (6)

WhereXi= As beforeZ1i = As beforeZ2i = As beforeW11 = Share price dummy*Times talk by the user-seller)W12 = Fixed price dummy*Times talk by the user-seller)ϵi is the sum of two error terms

Division name Irrigation (Hrs)* Labour (Man-day)

Own paym

ent

Crop share

Fixed charge

Two part tariff

Own payment

Crop share

Fixed charge

Two part tariff

Chittagong 365 99 276 325 101 128 157 149

Dhaka 373 372 289 325 107 122 111 103

Khulna 561 480 430 451 131 128 130 124

Rajshahi 313 323 171 217 104 98 101 93

Rangpur 393 0 293 373 106 0 107 113

All 409 354 294 363 111 109 115 112

Table . Division-wise inputs use under different payment system (Figure per hectare)

Division name

Likelihood percent of refusing irrigation water*

Users’ plot visit by the seller(No.)

Crop share

Fixed charge

Two part tariff

Crop share Fixed charge

Two part tariff

Chittagong 0 11.9 2.3 12 35 33

Dhaka 4.2 6.7 5.0 57 46 36

Khulna 4.7 5.9 3.2 66 48 44

Rajshahi 7.4 5.9 2.8 43 46 49

Rangpur - 8.7 2.7 - 49 46

All 6.0 7.1 3.7 50 46 41

Table 15 Likelihood percent of refusing irrigation water and users’ plot visit by the seller in different payment systems (Figure per season)

Payment

types

Target variables

Irrigation Labour Yield

(Hrs/ha) %∆ Man-

day/ha

%∆ Kg/ha %∆

Crop share 382 16.6 118 15.3 6075 -2.2

Fixed charge 317 15.4 119 5.6 6576 6.9

Two part

tariff 435 36.1 112 0.7 6187 -0.4

All 368 22.2 117 5.8 6344 2.5

Table 16. Changes situation of driving variables if visit by the seller to the user’s plot increase (25%) in different payment systems

Sensitivity analysis

Payment

types

Target variables

Irrigation Labour Yield

(Hrs/ha) %∆ Man-

day/ha

%∆ Kg/ha %∆

Crop share321 -2.0 102 0.5 6223 0.2

Fixed charge271 -1.3 112 -0.4 6153 -0.03

Two part

tariff 319 0.02 112 0.3 6190 -0.4

All298 -0.9 110 -0.01 6179 -0.1

Table 17. Changes situation of driving variables if visit by the seller to the user’s plot decrease (25%) in different payment systems (Figure per season)

Payment types Target variables

Irrigation Labour Yield

(Hrs/ha) %∆ Man-day/ha %∆ Kg/ha %∆

Crop share225 -43.9 106 -4.7 6167 -0.3

Fixed charge213 -32.9 112 -5.2 6204 -1.3

Two part tariff334 -10.5 109 -4.0 6246 1.4

All245 -31.1 110 -4.5 6206 -0.2

Table 18. Changes situation of driving variables if likelihood percent of refusing irrigation decrease (6% to 12%) in different payment systems (Figure per season)

Payment

types

Target variables

Irrigation Labour Yield

(Hrs/ha) %∆ Man-

day/ha

%∆ Kg/ha %∆

Crop share150 -62.6 111 -0.2 6043 -2.3

Fixed charge220 -30.8 118 0.0 5990 -4.7

Two part

tariff 316 -15.2 125 9.8 5979 -2.9

All221 -37.8 118 2.3 5998 -3.5

Table 19. Changes situation of driving variables if likelihood percent of refusing irrigation increase (6% to 18%) in different payment systems (Figure per season)

Payment types Likelihood of refusing Visit Irrigation

(Hrs)

Labour

(Man-day)

Yield

No. % ∆ No. %∆ No. %∆ No. %∆ No. %∆

Crop share 5.1 -0.9 45.8 0.9 355 0.4 110 0.1 6228 -0.3

Fixed charge 6.5 -0.5 42.3 0.9 291 2.3 117 0.2 6340 0.2

Two part tariff 3.6 1.1 35.2 1.5 346 1.3 114 -0.4 6227 0.3

All5.2 -0.4 40.7 1.0 322 1.6 115 0.0 6279 0.1

Table 20. Changes situation of driving variables if number of talk times increase 20% (27 to 36) in different payment systems (Figure per season)

Payment

types

Likelihood of refusing Visit Irrigation

(Hrs)

Labour (Man-day) Yield

No. % ∆ No. %∆ No. %∆ No. %∆ No. %∆

Crop share 5.2 0.0 45.4 0.0 354 0.0 110 0.0 6246 0.0

Fixed

charge

6.5 -0.7 42.3 0.9 283 -0.3 116 -0.3 6337 0.2

Two part tariff

3.6 2.3 34.7 -0.1 346 1.4 114 -0.3 6266 0.9

All 5.3 0.5 40.5 0.6 318 0.3 114 -0.3 6295 0.4

Table 21. Changes situation of driving variables if number of talk times decrease 20% (27 to 14) in different payment systems (Figure per season)

Dependent variable: Yield (kg/ha)Name of variables

Values of the coefficients in different models

I II III IV

Irrigation (hour) 0.34** 0.20 0.17 0.17Labour (Man-day) -0.75 -0.14 -0.08 -0.24Seed (kg) -5.08** -4.78* -4.79* -5.20*

Tillage (hour) 3.41 8.75 10.87 12.09Fertilizer (kg) 0.85*** 0.77** 0.77** 0.78**

Other fertilizer (kg) 0.02 0.03 0.03 0.03Insecticide & herbicides (kg/lit) 4.27 5.37 6.68 6.57Crop share payment dummy -215.21 70.72 129.04 212.10Fixed charge dummy -9.55 -84.37 -34.57 11.78Two part tariff dummy -162.81Crop share payment dummy* No. of visit by the seller 4.52** 4.49** 4.90**

Fixed charge dummy* No. of visit -2.49 -2.71 -1.99Share payment dummy*Likelihood percent of refusing irrigation

-7.31 -6.58

Fixed charge dummy* Likelihood percent of refusing irrigation

-8.25 -5.31

Share cropping dummy*No. of talk between user and seller

-2.40

Table 22. Summary of the variables and coefficients in different models

Inpu

ts on

ly

Supe

rvisi

on

Com

mitm

ent

Tran

sacti

on

Fixed renting dummy* No. of talk between user and seller

-4.87

Sandy loam soil dummy -283.89** -275.75** -267.48** -251.66*

Clay loam soil dummy -68.70 76.76 97.42 94.76Clay soil dummy 34.49 158.11 153.42 168.99Medium high land dummy -148.04 60.78 58.54 58.05High land dummy 31.77 226.10 239.68 252.06Farm size (ha) 231.71* 522.36** 536.13** 561.43**

Household head education (years schooling) 30.20*** 32.99** 33.57** 33.06**

Kinship dummy 191.32* 230.82* 233.44* 236.68**

Irrigation distance (meter) -0.13 -0.18 -0.17 -0.18Constant term 5935.73 5427.50 5390.53 5383.78

No. of observation 958 716 716 716Probability of F value 0.0000 0.0001 0.0002 0.0002R2 0.0570 0.0713 0.0736 0.0771Adjusted R2 0.0379 0.0445 0.0442 0.0450

There is a possibility to increase yield by increasing irrigation hours

III. Determinants of technical inefficiency:

Functional form of the model:

ii

n

jijji vXY

1

lnln 0

where: ln = natural logarithmic formYi = rice production (yield) in tons ha-1

k = number of input variablesβ0 = intercept or constant termβj = unknown parameters to be estimatedXij = vector of production inputs (j) of the farmer i

vi = random error termui = inefficiency component

Translog production function:

We can generalized it in the following form like as,

lnYi = β0 + β1lnX1i + β2lnX2i +0.5 β11(lnX1i)2 + 0.5 β22(lnX2i)2 + β12lnX1ilnX2i + vi - μi

ii

k

jiij

k

i

k

iiii vXXX jY

lnlnln

1110 2

1ln

Where,μi = technical inefficiencyδ0 = intercept or constant termδj = parameters to be estimatedZj = determinants of inefficiency

k

iijiji Z

10

Technical inefficiency model

Likelihood ratio (LR) test for selecting model between Cobb-Douglas & Translog since they belong nested

one another

Empirical models specification: Cobb-Douglas

lnYi =β0 + β1lnX1i + β2 lnX2i + β3 lnX3i + β4lnX4i + β5lnX5i + β6lnX6i + β7lnX7i + β8lnX8i + β9 lnX9i + β10lnX10i + β11lnX11i + vi – μi .. ... ... ...(11)Where,Yi = Yield (kg)X1i = Seed (kg/ha)X2i = Human labour (man-day/ha)X3i = Tillage (hour/ha)X4i = Irrigation (hour/ha)X5i = Chemical fertilizer (kg/ha)X6i = Insecticide & herbicides (kg or lit/ha)X7i = Other fertilizer dummy (1=use other fertilizer, 0= otherwise)X8i = Other cost dummy (1=use other cost, 0=otherwise)X9i = Share payment dummy (1=under share payment, 0=otherwise)X10i = Fixed charge dummy (1=under fixed charge payment, 0=otherwise)X11i = Two part dummy (1=under two part tariff payment, 0=otherwise)

Input variables Interaction factor variables1. Seed 12. 0.5*Seed2, 13. Seed*Human labour, 14. Seed*Tillage, 15. Seed*Irrigation, 16. Seed*Chemical fertilizer,

17. Seed* Insecticide & herbicides, 18. Seed* Other fertilizer dummy, 19. Seed* Other cost dummy, 20. Seed* Share payment dummy, 21. Seed* Fixed charge dummy, 22. Seed* Two part dummy

2. Human labour 23. 0.5*Human labour2, 24. Human labour*Tillage, 25. Human labour*Irrigation, 26. Human labour*Chemical fertilizer, 27. Human labour*Insecticide & herbicides, 28. Human labour*Other fertilizer dummy, 29. Human labour*Other cost dummy, 30. Human labour*Share payment dummy, 31. Human labour* Fixed charge dummy, 32. Human labour*Two part dummy

3 . Tillage 33. 0.5*Tillage2, 34. Tillage*Irrigation, 35. Tillage*Chemical fertilizer, 36. Tillage*Insecticide & herbicides, 37. Tillage* Other fertilizer dummy, 38. Tillage*Other cost dummy, 39. Tillage* Share payment dummy, 40. Tillage*Fixed charge dummy, 41. Tillage* Two part dummy

4. Irrigation 42. 0.5*Irrigation2, 43. Irrigation* Chemical fertilizer, 44. Irrigation* Insecticide & herbicides, 45. Irrigation*Other fertilizer dummy 46. Irrigation*Other cost dummy, 47. Irrigation* Share payment dummy, 48. Irrigation*Fixed charge dummy, 49. Irrigation* Two part dummy

5. Chemical fertilizer 50. 0.5*Chemical fertilizer2, 51. Chemical fertilizer*Insecticide & herbicides, 52. Chemical fertilizer*Other fertilizer dummy, 53. Chemical fertilizer*Other cost dummy, 54. Chemical fertilizer* Share payment dummy, 55. Chemical fertilizer* Fixed charge dummy, 56. Chemical fertilizer* Two part dummy

6. Insecticide & herbicides 57. 0.5*Insecticide & herbicides2, 58. Insecticide & herbicides* Other fertilizer dummy, 59. Insecticide & herbicides*Other cost dummy, 60. Insecticide & herbicides* Share payment dummy, 61. Insecticide & herbicides* Fixed charge dummy, 62. Insecticide & herbicides* Two part dummy

7. Other fertilizer dummy 63. Other fertilizer dummy*Other cost dummy, 64. Other fertilizer dummy*Share payment dummy, 65. Other fertilizer dummy*Fixed charge dummy, 66. Other fertilizer dummy*Two part dummy

8. Other cost dummy 67. Other cost dummy*Share payment dummy, 68. Other cost dummy* Fixed charge dummy, 69. Other cost dummy*Two part dummy

9. Share payment dummy -10. Fixed charge dummy -11. Two part dummy -

Empirical models specification: Translog

lnYi = β0 + β1lnX1i + β2lnX2i +0.5 β11(lnX1i)2 + 0.5 β22(lnX2i)2 + β12lnX1ilnX2i + ... + vi - μi ...(6)

Table 2. List of variables and interaction factors:

Table 23. Results from Translog modelDept: Yield (kg/ha)

Number of observation =958Wald chi-square =121.52Probability > chi-square = 0.0001 Log likelihood = -9.745668

Input variables and integration variables

Coefficient. Std. Err. z P>z

Two part dummy -1.028** 0.437 -2.350 0.019Seed-tillage -0.080*** 0.027 -2.960 0.003Seed-irrigation 0.033** 0.016 2.000 0.046Seed-two part tariff dummy 0.057* 0.032 1.780 0.075Labour-irrigation 0.076** 0.037 2.090 0.037Labour-chemical fertilizer 0.112** 0.068 1.640 0.102Tillageha2 -0.063* 0.038 -1.640 0.101Tillage-other fertilizer -0.099** 0.037 -2.700 0.007Tillage-two part tariff dummy 0.130*** 0.046 2.820 0.005Irrigation-other fertilizer -0.039* 0.022 -1.780 0.074Irrigation-share payment dummy -0.069** 0.034 -2.050 0.040Chemical fertilizer-other fertilizer 0.107** 0.050 2.150 0.031Constant term 12.232 1.633 7.49 0.00

Payment methods Technical efficiency TE Rank Technical inefficiency TI Rank

Own payment 0.767 2 0.232 3

Crop share 0.763 4 0.237 1

Fixed charge 0.768 1 0.231 4

Two part tariff 0.766 3 0.234 2

All 0.767 - 0.233 -

Table 24. Technical efficiency, inefficiency and rank

020

4060

80Fr

eque

ncy

0 .2 .4 .6 .8Inefficiency level

Inefficiency distribution of own payment system

010

2030

Freq

uenc

y

0 .2 .4 .6Inefficiency level

Inefficiency distribution of share payment system0

2040

6080

100

Freq

uenc

y

0 .2 .4 .6 .8Inefficiency level

Inefficiency distribution of fixed payment system

020

4060

80Fr

eque

ncy

0 .2 .4 .6 .8 1Inefficiency level

Inefficiency distribution of two part tariff payment system

Fig 22a-d. Frequency distribution of technical inefficiency

Tobit model for determining technical inefficiency:

lnYi =β0 + β1lnX1i + β2 lnX2i + β3 lnX3i + β4lnX4i + β5lnX5i + β6lnX6i + β7lnX7i + β8lnX8i + β9 lnX9i + β10lnX10i + μi ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... (11)Where,Yi = Technical inefficiency [Censored values, ll(o) & ul(1)]X1i = Sandy loam soil type dummy (1=sandy loam soil, 0=otherwise)X2i = Clay loam soil type dummy (1=clay loam soil, 0=otherwise)X3i = Clay soil type dummy (1=clay soil, 0=otherwise)X4i = Medium high land type dummy (1=medium high land, 0=otherwise)X5i = high land type dummy (1=high land, 0=otherwise)X6i = Farm size (ha)X7i = Kinship dummy (1=kinship, 0= otherwise)X8i = Family head age (year)X9i = Family head education (year of schooling)X10i = Distance from plot to tubewell (meter)X11i = Asset position of the farmer (Tk.)X12i = Loan dummy (1=loan receiver, 0=otherwise)μi = Error term

Determinants of inefficiency Coefficients Std. Err. t P>tSandy loam soil type dummy 0.0209** 0.0105 1.99 0.047Clay loam soil type dummy 0.0163 0.0132 1.23 0.219Clay soil type dummy -0.0058 0.0115 -0.51 0.611Medium high land type dummy 0.0121 0.0091 1.33 0.185High land dummy 0.0082 0.0134 0.62 0.538Farm size (ha) -0.0068 0.0066 -1.03 0.301Respondent’s age 0.0162 0.0153 1.06 0.29Respondent’s education -0.0128*** 0.0046 -2.78 0.006Kinship dummy -0.0178* 0.0095 -1.88 0.061Distance from plot to tubewell 0.0021 0.0024 0.89 0.373Asset position of the farmer -0.0083* 0.0049 -1.7 0.089Loan dummy 0.0003 0.0084 0.03 0.975

Table 25. Determinants of technical inefficiency in irrigated HYV boro rice by using Tobit model

Summary and conclusions:Major determinants for price of irrigation: Cobb-

DouglasFixed charge and two part tariff are less costly, tubewell

owner ratio, number of buyers, command area of a tubewell, remittance earning person, low land area, loam soil area, fuel priceMajor determinants of contract choice: Probit model

Crop share: Flood, Drought, (-) arsenic, low landFixed charge: Commitment level, Fuel price, (-) drought

Findings from multinomial probitCrop share: Irrigation cost, years with irrigated rice, flood,

droughtFixed charge: Yield, commitment level, fuel price

(electricity), low landTwo part tariff: Transaction cost (good relation), years with

irrigated rice production

Production efficiency/inefficiency issues: Sensitivity analysis:Labour and irrigation usages are less in crop share payment

system. Increasing interaction can increase it but will not be as like as land tenancy markets due to the different nature of water markets.

YieldFrom OLS: Irrigation, fertilizer, supervision (crop share),

farm size, farmers’ education, kinshipFrom Translog: Seed-irrigation, labour-irrigation, tillage-

two part tariff, chemical fertilizer-other fertilizer

TE: Higher in fixed charge payment and lower in crop share system

TI: Lower in fixed charge system and higher in crop share system

Determinants of inefficiency: Tobit model

Increase: Sand soil, high land, distance of the irrigation source

Decrease: Farmers’ education level, kinship, asset position

Some policy suggestions:Credit availability needs to be taken care more and interest rate of the NGOs needs to be lower and it needs monitoring from the government.

Paddy price at harvesting time is a big concerned for the farmers. The paddy procurement price can be declared at the beginning of the harvesting season otherwise farmers may shift in alternatives crop.

Irrigation water contracts is verbal. If water market works rationally, there will have less possibility of disputes. Market can enforce to increase the commitment level.  Groundwater is scarce resource. It needs deep thinking to use groundwater efficiently. AWD is one of the alternatives and we can get its application more by expanding two part tariff payment.

Reasonable irrigation water price per hectare can be suggested by the Ministry of Agriculture but it will be an intervention. Better try to improve the bargaining power of the users so that they can negotiate more rationally at local level.

New form of cash payment, two part tariff has more flexibility for both user and seller’s point of views and buyer has more benefits in this payment system.

Encouraging farmers for talking between user and seller for their activities that can increase the use of water and labour for increasing yield.

Acknowledgement

Fund support:1. GRiSP, IRRI2. ICRISAT3. VDSA

PhD admission & leave permission:Dept. of Agri. EconomicsBangladesh Agricultural University

Prof. Dr. Randolph BarkerDr. Elizabeth Liz, CESD

Dr. David Spielman, IFPRIDr. Uttam Deb, ICRISATDr. Motaleb, CIMMYT

Orlee, ADB, Anylin, Floilan, TC

Dr. Khan, BAU Dr. Akhlas, BRRI Rouselle, Zilhas,

Robert, UPLB

Finally hospitality at SSD is gratefully acknowledged

Data Source Team

Thanks for patience hearing